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Machines
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Artificial Intelligence for Automatic Control of Vehicles
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Artificial Intelligence for Automatic Control of Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 21351

Special Issue Editors

Prof. Dr. Palkovics László

E-Mail Website
Guest Editor
Department of Road and Rail Vehicles, Széchenyi István University, Győr, Hungary
Interests: autonomous vehicle
Prof. Dr. István Lakatos

E-Mail Website
Guest Editor
Department of Road and Rail Vehicles, Széchenyi István University, Győr, Hungary
Interests: vehicle diagnostics; alternative drive systems; internal combustion engines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI and machine learning also play a significant role in Autonomous Vehicles. Autonomous technology is a promising one and it hopefully leads to safe, easy, and sustainable transportation. Thus, the Special Issue focuses on architectures, algorithms and technologies for AI and Autonomous Vehicles.

AI enabling intelligent networks. AI and machine learning techniques will improve automation and ultimately enable the zero-touch automation required to manage billions of connected devices and handle requirements from new 5G use cases and the increased network complexity. As 5G, IoT and Edge gains traction, the shift that transforms industries and enterprises, becomes a reality. It also brings new complexities of network operations – co-existing of new and legacy technologies, hybrid networks, a variety of frequency bands and spectrums, and an abundance of connected devices. To realize the full potential of AI, trust needs to be established in the development, deployment and use of AI. This is critically, why we build human trust in AI addressing aspects spanning from explainability and human oversight to security, and built-in safety mechanisms. Connected vehicle technology is accelerating, powered by 5G – faster, safer, more accessible and more secure. Reliable communication means guaranteed delivery of time-critical information. 5G cellular technology is designed from day one for ultra-reliable communication with low latency to enable complex machine centric use cases, including autonomous cars in dense urban as well as high speed scenarios.

Prospective authors from both industry and academia are invited to submit original manuscripts on this topics including:

  • AI and machine learning techniques for Vehicles;
  • AI-empowered approach to control of Vehicles;
  • 5G, IoT and Edge computing for Autonomous Vehicles;
  • Cooperative perception and collaborative behavior of Autonomous Vehicles;
  • Autonomous freight transportation and delivery;
  • AI and traditional approach used together in sensor fusion for Vehicles.

Prof. Dr. Palkovics László
Prof. Dr. István Lakatos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • autonomous freight transportation
  • 5G
  • collaborative vehicles
  • road environment recognition
  • LIDAR
  • ADAS
  • camera

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Published Papers (5 papers)

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Research

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21 pages, 2547 KiB  
Article
Brake Disc Deformation Detection Using Intuitive Feature Extraction and Machine Learning
by Tamás Dózsa, Péter Őri, Mátyás Szabari, Ernő Simonyi, Alexandros Soumelidis and István Lakatos
Machines 2024, 12(4), 214; https://doi.org/10.3390/machines12040214 - 22 Mar 2024
Viewed by 1446
Abstract
In this work we propose proof-of-concept methods to detect malfunctions of the braking system in passenger vehicles. In particular, we investigate the problem of detecting deformations of the brake disc based on data recorded by acceleration sensors mounted on the suspension of the [...] Read more.
In this work we propose proof-of-concept methods to detect malfunctions of the braking system in passenger vehicles. In particular, we investigate the problem of detecting deformations of the brake disc based on data recorded by acceleration sensors mounted on the suspension of the vehicle. Our core hypothesis is that these signals contain vibrations caused by brake disc deformation. Since faults of this kind are typically monitored by the driver of the vehicle, the development of automatic fault-detection systems becomes more important with the rise of autonomous driving. In addition, the new brake boosters separate the brake pedal from the hydraulic system which results in less significant effects on the brake pedal force. Our paper offers two important contributions. Firstly, we provide a detailed description of our novel measurement scheme, the type and placement of the used sensors, signal acquisition and data characteristics. Then, in the second part of our paper we detail mathematically justified signal representations and different algorithms to distinguish between deformed and normal brake discs. For the proper understanding of the phenomenon, different brake discs were used with measured runout values. Since, in addition to brake disc deformation, the vibrations recorded by our accelerometers are nonlinearly dependent on a number of factors (such as the velocity, suspension, tire pressure, etc.), data-driven models are considered. Through experiments, we show that the proposed methods can be used to recognize faults in the braking system caused by brake disc deformation. Full article
(This article belongs to the Special Issue Artificial Intelligence for Automatic Control of Vehicles)
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16 pages, 5237 KiB  
Article
Deep Learning-Based Approach for Autonomous Vehicle Localization: Application and Experimental Analysis
by Norbert Markó, Ernő Horváth, István Szalay and Krisztián Enisz
Machines 2023, 11(12), 1079; https://doi.org/10.3390/machines11121079 - 9 Dec 2023
Cited by 1 | Viewed by 4202
Abstract
In a vehicle, wheel speed sensors and inertial measurement units (IMUs) are present onboard, and their raw data can be used for localization estimation. Both wheel sensors and IMUs encounter challenges such as bias and measurement noise, which accumulate as errors over time. [...] Read more.
In a vehicle, wheel speed sensors and inertial measurement units (IMUs) are present onboard, and their raw data can be used for localization estimation. Both wheel sensors and IMUs encounter challenges such as bias and measurement noise, which accumulate as errors over time. Even a slight inaccuracy or minor error can render the localization system unreliable and unusable in a matter of seconds. Traditional algorithms, such as the extended Kalman filter (EKF), have been applied for a long time in non-linear systems. These systems have white noise in both the system and in the estimation model. These approaches require deep knowledge of the non-linear noise characteristics of the sensors. On the other hand, as a subset of artificial intelligence (AI), neural network-based (NN) algorithms do not necessarily have these strict requirements. The current paper proposes an AI-based long short-term memory (LSTM) localization approach and evaluates its performance against the ground truth. Full article
(This article belongs to the Special Issue Artificial Intelligence for Automatic Control of Vehicles)
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19 pages, 5883 KiB  
Article
Development and Functional Validation Method of the Scenario-in-the-Loop Simulation Control Model Using Co-Simulation Techniques
by Balint Toth and Zsolt Szalay
Machines 2023, 11(11), 1028; https://doi.org/10.3390/machines11111028 - 17 Nov 2023
Viewed by 2011
Abstract
With the facilitated development of highly automated driving functions and automated vehicles, the need for advanced testing techniques also arose. With a near-infinite number of potential traffic scenarios, vehicles have to drive an increased number of test kilometers during development, which would be [...] Read more.
With the facilitated development of highly automated driving functions and automated vehicles, the need for advanced testing techniques also arose. With a near-infinite number of potential traffic scenarios, vehicles have to drive an increased number of test kilometers during development, which would be very difficult to achieve with currently utilized conventional testing methods. State-of-the-Art testing technologies such as Vehicle-in-the-Loop (ViL) or Scenario-in-the-Loop (SciL) can provide a long-term solution; however, validation of these complex systems should also be addressed. ViL and SciL technologies provide real-time control and measurement with multiple participants; however, they require enormous computational capacity and low-latency communication to provide comparable results with real-world testing. 5G (fifth-generation wireless) communication and Edge computing can aid in fulfilling these needs, although appropriate implementation should also be tested. In the current paper, a realized control model based on the SciL architecture was presented that was developed with real-world testing data and validated utilizing co-simulation and digital twin techniques. The model was established in Simcenter Prescan© connected to MATLAB Simulink® and validated using IPG CarMaker®, which was used to feed the simulation with the necessary input data to replace the real-world testing data. The aim of the current paper was to introduce steps of the development process, to present the results of the validation procedure, and to provide an outlook of potential future implementations into the state of the art in proving ground ecosystems. Full article
(This article belongs to the Special Issue Artificial Intelligence for Automatic Control of Vehicles)
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17 pages, 3928 KiB  
Article
Urban Platooning Combined with Dynamic Traffic Lights
by Husam Altamimi, István Varga and Tamás Tettamanti
Machines 2023, 11(9), 920; https://doi.org/10.3390/machines11090920 - 21 Sep 2023
Cited by 2 | Viewed by 1711
Abstract
Platooning is generally known as a control method for driving a group of connected and automated vehicles in motorway context. Nevertheless, platoon control might also work on urban roads. One possible strategy to increase overall road traffic performance and to reduce congestion in [...] Read more.
Platooning is generally known as a control method for driving a group of connected and automated vehicles in motorway context. Nevertheless, platoon control might also work on urban roads. One possible strategy to increase overall road traffic performance and to reduce congestion in urban traffic networks is to combine platooning with traffic signal control at intersections. The traffic flow can be maximized with coordinated scheduling of traffic signals together with platooning activities, resulting in decreased travel times and fuel consumption. This paper investigates several aspects of this combined control, such as the procedures for coordination and communication between platooning vehicles and traffic signals. Efficient algorithms are suggested to optimize platoon formation and dissolution at junctions and to change traffic signal phases depending on platoon arrival and departure times. The proposed solutions have been tested and verified with SUMO, a high-fidelity microscopic traffic simulator. Full article
(This article belongs to the Special Issue Artificial Intelligence for Automatic Control of Vehicles)
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Review

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29 pages, 775 KiB  
Review
Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review
by Faizan Sana, Nasser L. Azad and Kaamran Raahemifar
Machines 2023, 11(7), 676; https://doi.org/10.3390/machines11070676 - 23 Jun 2023
Cited by 9 | Viewed by 10831
Abstract
The development of autonomous vehicles (AVs) is becoming increasingly important as the need for reliable and safe transportation grows. However, in order to achieve level 5 autonomy, it is crucial that such AVs can navigate through complex and unconventional scenarios. It has been [...] Read more.
The development of autonomous vehicles (AVs) is becoming increasingly important as the need for reliable and safe transportation grows. However, in order to achieve level 5 autonomy, it is crucial that such AVs can navigate through complex and unconventional scenarios. It has been observed that currently deployed AVs, like human drivers, struggle the most in cases of adverse weather conditions, unsignalized intersections, crosswalks, roundabouts, and near-accident scenarios. This review paper provides a comprehensive overview of the various navigation methodologies used in handling these situations. The paper discusses both traditional planning methods such as graph-based approaches and emerging solutions including machine-learning based approaches and other advanced decision-making and control techniques. The benefits and drawbacks of previous studies in this area are discussed in detail and it is identified that the biggest shortcomings and challenges are benchmarking, ensuring interpretability, incorporating safety as well as road user interactions, and unrealistic simplifications such as the availability of accurate and perfect perception information. Some suggestions to tackle these challenges are also presented. Full article
(This article belongs to the Special Issue Artificial Intelligence for Automatic Control of Vehicles)
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